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A study on welding quality inspection system for shell-tube heat exchanger based on machine vision

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Abstract

An automated based-vision quality inspection system for Shell-Tube welding is presented in this paper in order to achieve nondestructive weld defect detection. The vision sensor is developed on the basis of the principle of laser triangulation. First, the composition and working principle of the vision-based quality inspection system are introduced, and meanwhile various defects may occur are described in detail. Then the image processing algorithm, as the most important part of online quality inspection system, is also represented. The image processing algorithm includes two parts: preprocessing and defect detection. In defect detection section, a novel method for determining and describing the position of undercut, which is based on the parameter equation of the circle to represent the position of the undercut, is presented. Last, two experiments are carried out for Shell-Tube heat exchanger and the experiments show that the image algorithm has high precision, strong robustness and can detect the defect accurately.

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References

  1. Cook, G. E., Barnett, R. J., Andersen, K., Springfield, J. F., and Strauss, A. M., “Automated Visual Inspection and Interpretation System for Weld Quality Evaluation,” Proc. of Industry Applications Conference, pp. 1809–1816, 1995.

    Google Scholar 

  2. Xu, W., Wang, X., and Zhang, C., “Overview of Typical Quality Problems in Nuclear Power Station Steam Generator Tube to Tube Sheet Welds,” Hot Working Technology, Vol. 42, No. 13, pp. 162–164, 2013.

    Google Scholar 

  3. Chang, D., Son, D., Kim, N., Kim, J., Lee, J., et al., “Development of a Characteristic Point Detecting Seam Tracking Algorithm for Portable Welding Robots,” Proc. of IEEE International Workshop on Robotic and Sensors Environments (ROSE), pp. 1–6, 2010.

    Google Scholar 

  4. Xu, S. Q., “A Design of Path Planning System for UP6 Industrial Robot Based on Machine Vision,” Advanced Materials Research, Vols. 591-593, pp. 1418–1421, 2012.

    Article  Google Scholar 

  5. Wang, J., Dou, H., Zheng, S., and Masanori, S., “Target Recognition Based on Machine Vision for Industrial Sorting Robot,” Journal of Robotics, Networking and Artificial Life, Vol. 2, No. 2, pp. 100–102, 2015.

    Article  Google Scholar 

  6. Li, Y., Li, Y. F., Wang, Q.L., Xu, D., and Tan, M., “Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection,” IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 7, pp. 1841–1849, 2010.

    Article  Google Scholar 

  7. Huang, W. and Kovacevic, R., “A Laser-Based Vision System for Weld Quality Inspection,” Sensors, Vol. 11, No. 1, pp. 506–521, 2011.

    Article  Google Scholar 

  8. Dar, I. M., Newman, K. E., and Vachtsevanos, G., “On-Line Inspection of Surface Mount Devices Using Vision and Infrared Sensors,” Proc. of Auto-Test Conference, pp. 376–384, 1995.

    Google Scholar 

  9. Valle, M., Gallina, P., and Gasparetto, A., “Mirror Synthesis in a Mechatronic System for Superficial Defect Detection,” IEEE/ASME Transactions on Mechatronics, Vol. 8, No. 3, pp. 309–317, 2003.

    Article  Google Scholar 

  10. Wong, B. K., Elliott, M. P., and Rapley, C. W., “Automatic Casting Surface Defect Recognition and Classification,” Proc. of IET Colloquium on ‘The Application of Machine Vision’, pp. 10/1-10/5, 1995.

    Google Scholar 

  11. Nguyen, H.-C. and Lee, B.-R., “Laser-Vision-Based Quality Inspection System for Small-Bead Laser Welding,” Int. J. Precis. Eng. Manuf., Vol. 15, No. 3, pp. 415–423, 2014.

    Article  Google Scholar 

  12. Senthil Kumar, G., Natarajan, U., and Ananthan, S., “Vision Inspection System for the Identification and Classification of Defects in MIG Welding Joints,” The International Journal of Advanced Manufacturing Technology, Vol. 61, No. 9, pp. 923–933, 2012.

    Article  Google Scholar 

  13. Rodríguez-Martín, M., Lagüela, S., González-Aguilera, D., and Rodríguez-Gonzálvez, P., “Procedure for Quality Inspection of Welds Based on Macro-Photogrammetric Three-Dimensional Reconstruction,” Optics & Laser Technology, Vol. 73, pp. 54–62, 2015.

    Article  Google Scholar 

  14. Chondronasios, A., Popov, I., and Jordanov, I., “Feature Selection for Surface Defect Classification of Extruded Aluminum Profiles,” The International Journal of Advanced Manufacturing Technology, Vol. 83, Nos. 1-4, pp. 33–41, 2016.

    Article  Google Scholar 

  15. Zhu, R. and Wang, Y., “Application of Improved Median Filter on Image Processing,” Journal of Computers, Vol. 7, No. 4, pp. 838–841, 2012.

    Google Scholar 

  16. Dong, Y. X., “Review of Otsu Segmentation Algorithm,” Advanced Materials Research, Vols. 989-999, pp. 1959–1961, 2014.

    Article  Google Scholar 

  17. Gonzalez, R. C. and Woods, R. E., “Digital Image Processing, Third Edition” pp. 407–411, 3rd Ed., 2007.

    Google Scholar 

  18. Naranbaatar, E., Kim, H.-S., and Lee, B.-R., “Radius Measuring Algorithm Based on Machine Vision Using Iterative Fuzzy Searching Method,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 6, pp. 915–926, 2012.

    Article  Google Scholar 

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Correspondence to Hui-Hui Chu.

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Chu, HH., Wang, ZY. A study on welding quality inspection system for shell-tube heat exchanger based on machine vision. Int. J. Precis. Eng. Manuf. 18, 825–834 (2017). https://doi.org/10.1007/s12541-017-0098-0

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  • DOI: https://doi.org/10.1007/s12541-017-0098-0

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